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precision machining, tools are prematurely discarded to avoid the risk of costly non-conformities. This creates a large loss of value of strategically valuable raw materials and waste of energy related
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Working Title PhD MSE - Research Assistant Location Edinburg, Texas College College of Engineering and Computer Science Department College of Engineering and Computer Science / Mechanical Engineering
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highly motivated candidate to develop models integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing
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offers a unique challenge—while liquid lithium strongly boots the tritium breeding, and lithium’s strong affinity for hydrogen isotopes implies little to no tritium loss to structural materials, it also
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Professors who will conduct academic research in association with a faculty member or team. The appointees will contribute to our translational research program in Developmental Therapeutics and Immuno
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processes, preferably HTPEM fuel cells. Proficiency in relevant computational tools, such as COMSOL Multiphysics. Familiarity with degradation mechanisms and the transient behavior of PEM fuel cell stacks is
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Neural Networks (SSM-SNNs). The project includes the co-design and integration of a RISC-V processor for hybrid neuromorphic computing. The research aims to develop ultra-low-power computing chips
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to the faculty's Doctoral Programme Proficiency in spoken and written English. PLEASE NOTE: For detailed information about what the application must contain, see paragraph “About the application”. The appointment is
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Testing (NDT) – a crucial technique used in industries to assess materials, components, and structures without causing damage. This PhD project focuses on advancing Ultrasound Testing and X-ray Computed
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according to how well they meet the following criteria: A first class or strong upper second-class undergraduate degree with honours in Engineering, Physics or Materials Science Excellent English written and